1. Field of the Invention
The present invention provides systems, methods, and devices for electroporation-based therapies (EBTs). Embodiments provide patient-specific treatment protocols derived by the numerical modeling of 3D reconstructions of target tissue from images taken of the tissue, and optionally accounting for one or more of physical constraints and/or dynamic tissue properties. The present invention further relates to systems, methods, and devices for delivering bipolar electric pulses for irreversible electroporation without damage to tissue typically associated with an EBT-induced excessive charge delivered to the tissue and mitigate electrochemical effects that may distort the treatment region.
2. Description of Related Art
Irreversible electroporation (IRE) and other electroporation-based therapies (EBTs), such as electrogenetransfer or electrochemotherapy, may often be administered in a minimally invasive fashion. There are, however, several considerations that may lead to an increase in the difficulty of administering such treatments. This includes typical applications where deep targeted regions are treated by placing needle or other electrodes deep into the tissue, where one can no longer directly visualize the affected area. There is some evidence that changes in the tissue's permeability, and therefore also its electrical conductivity, allow one to visualize and monitor affected regions in real-time. These changes are most pronounced in homogeneous and image-dense tissues, such as hyperechoic ultrasound tissues, where increased permeability decreases the electroporated echogenicity. However, many tumors and other tissues are far too heterogeneous or exhibit properties that do not allow for simple visualization of the electroporated areas. In addition, these changes for real-time imaging typically only designate electroporated regions, not necessarily those killed for IRE therapies.
In applying EBTs, ensuring adequate coverage of the targeted region (e.g., any mass or lesion or undesirable tissue to be affected, including margins beyond the lesion itself), while sparing healthy tissues is vital to therapeutic success. Due to the limitations inherent in treating deep tissues without exposing them, it is critical for practitioners to develop and implement treatment protocols capable of achieving their clinical objectives.
Furthermore, typical electrodes and pulsing parameters (number of pulses, pulse polarity, pulse length, repetition rate, pulse shape, applied voltage, electrode geometry and orientation, etc.) will have a large impact on the affected areas. Typical therapeutic geometries dictated by current electrode setups will be ellipsoidal in general shape. However, many tumors do not distinctly fit the shapes created by a single setup of an electrode. Therefore, successful implementation of EBTs typically requires a complex array of electrodes and pulse parameters arranged in a specific manner to ensure complete treatment of the targeted area while minimizing effects to healthy tissue and sparing vital structures. Such predictions of superimposing treatment regions for complex protocols can be cumbersome. Therefore, treatment planning techniques that aid or allow a practitioner to develop general treatment protocols for most clinical tumors are typically used to effectively capitalize on the great therapeutic potential for IRE and other EBTs.
Current treatment planning techniques from systems such as the NanoKnife® utilize interpolations and analytical techniques to aid practitioner treatment region predictions. The interpolation techniques provide the physician with diagrams of 3D numerical model solution predicted treatment areas from very specific settings, including an exact number of pulses, pulse length, voltage, and electrode setup (e.g., separation distance, exposure length, and diameter) with dimensions provided for the treatment areas in 2 planes and the general shape. The predicted treatment dimensions are taken from the experimental results of applying that specific set of conditions in experimental subjects, typically in healthy, homogeneous environments. It is from this diagram of expected region, that the physician would set up their electrodes the same way and use the same pulses and arrange multiple applications to the point where they anticipate they will have treated the entire volume.
There is room, however, for improvement in such systems. If the targeted volume is smaller than the dimensions in the diagram, the practitioner has no information about how much to change the physical setup (exposure length, separation distance, etc.), or pulse parameters (voltage, number of pulses, etc.) in order to prevent damaging the surrounding tissue. In another example, if the shape does not fit that of the diagram, the practitioner will not be able to adjust the protocol to minimize damage beyond the targeted margin while still treating the targeted area.
In another solution to facilitating practitioner treatment planning, software is provided that uses a lookup table of treatment dimensions or uses a calibrated analytical solution to mimic the shape of numerical simulations. The lookup table may be taken from a large compilation of simulations run at varying physical and pulse parameters, where dimensions of interest for predicted treatment regions are taken based on a calibrated electric field threshold found to represent the affected margin of interest observed in experiments on healthy tissue (IRE, reversible electroporation, no electroporation, thermal damage).
Although the lookup table would allow a practitioner to manipulate the above variables and receive real-time feedback on predicted dimensions, the geometry of the affected region is often more complex than can be summarized with a few dimensions. Therefore, analytical solutions for the shape of the electric field distribution have been developed and are the current state-of-the-art on the NanoKnife® system. These solutions are able to mimic the shape of the electric field distribution from typical numerical simulations. The value of electric field contour is then matched to that seen from the numerical solution so that they both respond to their physical and pulse conditions in approximately the same manner. A calibration can then be used to adjust the size, and therefore various electric field thresholds (IRE, reversible, no electroporation, thermal damage) depicted to provide predicted affected regions. The practitioner may then adjust the variables such as voltage and separation distance (currently the only two that account for changes in predicted margins in the NanoKnife® embodiment), and see how the predicted affected margins vary in real-time. This provides the practitioner a much better method to find and place an appropriate electrode array with variable voltages to treat the entire region. There is also an optimization autoset probes function that places the probes and sets the voltage based on the number of probes selected and three dimensions input for the targeted region (assuming it to be a perfect ellipsoid).
The current state-of-the-art provides a very basic, fundamental explanation to practitioners about predicted treatment regions. Application of the current techniques in real-life clinical and experimental scenarios in which EBTs will typically be used provides to the practitioner helpful but inflexible tools.
For example, the analytical embodiment is a simple cross-sectional view of predicted margins at the center of the electrodes. This means that it cannot account for the falloff of electric field distribution (and therefore affected margins) at the tips of the electrodes. Although use of this approach can mimic the shape and size of these regions in 2D, it is not possible to accurately depict 3D scenario shapes in detail. Further, the lookup table cannot easily provide an accurate 3D shape, nor can the analytical solution be adapted.
True electroporation applications will increase the conductivity of the affected regions, which will in turn change the size and shape of the electric field distribution. A comparison of the electric field distribution (A,C) and conductivity map (B,D) of two identical numerical models without (A,B) and with (C,D) changing conductivity is shown in FIGS. 1A-D. From these figures, one can see how the conductivity increases from 0.1 S/m (the baseline level for the entire tissue domain, constant in part B) up to 0.155 S/m, an increase of 55%, for regions experiencing predicted IRE (deep red in part D), with regions experiencing varying extents of predicted reversible electroporation filling in between this (cyan through bright red). This change in conductivity in response to electroporation effects results in an altered electric field distribution, which may be seen in part C, where the distribution is larger, especially at the region between the electrodes. Changes in conductivity have been observed to reach several times higher than the baseline conductivity in the literature. These changes can be simulated in numerical solutions, and the general size changes can be accounted for with some accuracy in the analytical solutions by recalibrating them, but their shape is fixed, and cannot accurately reflect the predicted affected region's shape when considering changing conductivity.
Tumors will often have different electrical and physical properties than their neighboring tissues or even from their native tissues of origin (e.g., cancerous astrocytes which may not behave the same as normal ones). In addition, surrounding tissues of different tissue types will also have different properties from each other (bone, muscle, fat, blood). These differences in electrical properties will alter the electric field distribution for a given application of EBTs. Because the electric field to which the tissue is exposed is the primary determinant in the effect on the cell, these changes will change the shape and size of the affected regions. Numerical simulations are capable of modeling the electric field distribution in such heterogeneous systems. However, the rigid analytical solutions cannot be adjusted to account for such differences, and therefore could not as accurately predict affected regions for the different environments in clinical cases. The analytical solution, e.g., could not predict the differences between a tumor situated adjacent to the skull, the quadriceps muscle, or the heart. Although lookup tables could theoretically be developed for the dimensions of the affected regions in a number of environments, the great variability between the anatomy of each patient, each specific tumor, and each exact tumor location relative to its environment is impractical and futile.
FIGS. 2A-J demonstrate the effect of heterogeneous systems on electric field distribution. These figures show the electric field and temperature distribution for a three-dimensional numerical model. More particularly, FIG. 2J shows the model setup, where two needle electrodes (1 mm in diameter) are placed within the outer borders of a targeted region of tissue, surrounded by a peripheral region. The red and black regions on the electrodes represent the energized surfaces, where 4200 V was applied to one electrode and the other was set to ground. The thermal properties were set to represent a targeted region of a tumor within fat. The electrical conductivity for the targeted (σt) and peripheral (σp) tissues was manipulated between 0.025 and 0.25 S/m to establish conductivity ratios (σt/σp; relative conductivities of the targeted/peripheral region) of 0.1, 1, and 10. FIGS. 2A-I show the numerical model outputs for conductivity ratios (σt/σp) of 0.1 (A,D,G), 1 (B,E,H), and 10 (C,F,I) showing electric field (A-F) during the pulse and temperature (G-I) distributions 1 second after the first pulse. The higher conductivity ratios show progressively more area treated by IRE with less thermal effects. Targeted tissue boundary may be seen as the solid black line. Observing the electric field distribution at the boundary shows that the shape is also changing (not just size) as a result of the heterogeneous environment. Existing treatment planning systems are not capable of accounting for such dynamic tissue properties in real time.
The current embodiment of the treatment planning software still leaves it up to the practitioner to select a desired number of probes, but provides no simple method of showing how the optimized distributions will be shaped if the user wants to directly compare using different numbers of probes for a given lesion. The current system therefore also does not select the optimal number of probes for the user, a question that may be difficult to answer for more complex electrode geometries.
Temperature changes associated with Joule-type resistive heating of the tissue will also affect local regions conductivity based on its temperature (typically increases by approximately 3%/° C.). This will also change the size and shape of the electric field distribution based on the parameters used; including the number of pulses, pulse length, and repetition rate for an entire protocol (more pulses of longer length with higher repetition rates will all increase the thermally-associated conductivity changes, increasing this variation). Because the current treatment planning tools are based on simulations from the electric field distribution of a single application of a pulse, these dynamic conductivity behaviors also cannot be taken into account. Something that does would have to be able to simulate the changes that occur as a result of thermal effects on conductivity.
The current state of the art does allow the practitioner to describe the size/shape of the lesion in very basic dimensional terms (length, width, depth). This shape is then superimposed to scale with the predicted treatment regions, allowing a practitioner to ensure appropriate distribution and coverage. Although we have already pointed out the insufficiencies in handling this third dimension, it should also be pointed out that the basic ellipsoidal shape assumed by this system is wholly inadequate at describing the complex, often irregular, asymmetric geometries that tumors may take in clinical settings. The practitioner is thus left currently with assessing treatment protocol adequacy in 2D terms.
What is needed is a technique and system (or a series of independent systems) that allows a practitioner to accurately plan and implement in real time patient-specific treatment protocols which are capable of accounting for dynamic tissue properties and which can be used with accuracy and reliability in the clinical or experimental setting for EBTs.